Approaches to working in high-dimensional data spaces: gene expression microarrays

Wang, Y; Miller, David J.; Clarke, Robert

doi:10.1038/sj.bjc.6604207

Cited by 63 publications

(50 citation statements)

References 28 publications

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“…Analytic approaches to discover new subclasses of samples based on distinct patterns of gene expression have been extensively used in cancer genomics to identify new categories of tumors types with distinct molecular, prognostic, or drug-response features [7,8,9,29,30]. Clustering approaches share one critical aspect: the analysis is unsupervised.…”

Section: Subclass Discovery Using Microarray Datamentioning

confidence: 99%

Deconvolving heterogeneity in the CD8+ T-cell response to HIV

Haining

Barnitz

2012

Current Opinion in HIV and AIDS

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Purpose of review This review will discuss the use of systems biology approaches to dissect the heterogeneity of the HIV-specific CD8+ T cell response. Recent findings New experimental approaches have allowed complex phenotypes of individual cells present in the human antigen-specific CD8+ T cell response to be characterized. Genome-wide measurements of gene expression in antigen-specific T cells have created broad molecular phenotypes of the T cell response to HIV. Pattern recognition algorithms to discover new sub-classes of samples in microarray datasets are becoming increasingly sophisticated. Together, these advances now allow the heterogeneity of the T cell response to HIV to be unraveled. Summary The phenotype antigen-specific T cells responding to pathogens like HIV in humans is seen as much “noisier” than in animal models of infection. However, applying new systems biology tools may provide an opportunity to identify the sources of this “noise” as novel, biologically distinct sub-classes of CD8+ T cell response to HIV.

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Section: Subclass Discovery Using Microarray Datamentioning

confidence: 99%

Deconvolving heterogeneity in the CD8+ T-cell response to HIV

Haining

Barnitz

2012

Current Opinion in HIV and AIDS

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“…In the past few years, many classical gene selection method based on clustering algorithm have been proposed to find relative genes. [4][5][6] These methods often run clustering method on a small subset, * Authors to whom correspondence should be addressed. and then select significant genes at each gene clustering.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Gene Selection Method for Cancer Classification Based on Clustering Algorithm and Euclidean Distance

Yang¹,

Cao²,

Li³

et al. 2012

j comput theor nanosci

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Classification for gene expression data is an important research filed in bioinformatics. Gene chip can massively detect the expression of thousands of genes in one experiment, and it has a very important practical significance for cancer classification and diagnosis. However, the gene expression data has many characteristics such as high-throughput, high dimensional, nonlinear, high noise and uneven distribution, which make it difficult to be processed. It is difficult to find the amount of feature genes, which have classification capability and minimum redundancy from the gene expression profile, and also play a key role in cancer diagnosis and research of pathogenesis mechanism. In this paper, we propose a new hybrid gene selection method using clustering algorithm. We first use filter method to rank the genes in terms of their expression difference, and then a clustering method is used in clustering gene expression data. Afterwards we select and substitute combinative genes according to Euclidean distance. The characteristics of gene selection methods are validated using the leukemia data set. The experimental results demonstrate the effectiveness of our method in addressing the problem.

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“…A large genotyping data set may consist of 2 million Single Nucleotide Polymorphism (SNP) probes by 10000 samples. Dimensionality is of concern to most analysis approaches, with the number of variables (genes, SNPs, sequences) vastly outweighing the number of observations [25].…”

Section: Introductionmentioning

confidence: 99%

A parallel random forest classifier for R

Mitchell

Sloan

Mewissen

et al. 2011

Proceedings of the Second International Workshop on Emerging Computational Methods for the Life Sciences

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The statistical language R is favoured by many biostaticians for processing microarray data. In recent times, the quantity of data that can be obtained in experiments has risen significantly, making previously fast analyses time consuming, or even not possible at all with the existing software infrastructure. High Performance Computing (HPC) systems offer a solution to these problems, but at the expense of increased complexity for the end user. The Simple Parallel R Interface (SPRINT) is a library for R that aims to reduce the complexity of using HPC systems by providing biostatisticians with drop-in parallelized replacements of existing R functions. In this paper we describe the implementation of a parallel version of the Random Forest classifier in the SPRINT library.

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Approaches to working in high-dimensional data spaces: gene expression microarrays

Cited by 63 publications

References 28 publications

Deconvolving heterogeneity in the CD8+ T-cell response to HIV

Deconvolving heterogeneity in the CD8+ T-cell response to HIV

A Hybrid Gene Selection Method for Cancer Classification Based on Clustering Algorithm and Euclidean Distance

A parallel random forest classifier for R

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